The key operation in Bayesian inference is to compute high-dimensional integrals. An old approximate technique is the Laplace method or approximation, which dates back to Pierre-Simon Laplace (1774). This simple idea approximates the integrand with a second-order Taylor expansion around the mode and computes the integral analytically. By developing a nested version of this classical idea, combined ...

The class of autoregressive (AR) processes is extensively used to model temporal dependence in observed time series. Such models are easily available and routinely fitted using freely available statistical software like R. A potential problem is that commonly applied estimators for the coefficients of AR processes are severely biased when the time series are short. This paper studies the ...

<p><b>Background</b> A healthy diet can decrease the risk of several lifestyle diseases. From studying the health effects of single foods, research now focuses on examining complete diets and dietary patterns reflecting the combined intake of different foods. The main goals of the current study were to identify dietary patterns and then investigate how these differ in terms of sex, age, educational ...

Context. Solar Orbiter provides dust detection capability in the inner heliosphere, but estimating physical properties of detected dust from the collected data is far from straightforward.<p> <p>Aims. First, a physical model for dust collection considering a Poisson process is formulated. Second, it is shown that dust on hyperbolic orbits is responsible for the majority of dust detections with ...

The ocean migration of 16 post-spawned adult Atlantic salmon [Salmo salar L.] from the Miramichi River, Canada, tagged concurrently with pop-up satellite archival tags and acoustic transmitters was reconstructed using a Hidden Markov Model. Individuals exclusively utilized areas within the Gulf of St Lawrence and the Labrador Sea, and showed little overlap with known distributions of European stocks. ...

In this paper, we introduce a new concept for constructing prior distributions. We exploit the natural nested structure inherent to many model components, which defines the model component to be a flexible extension of a base model. Proper priors are defined to penalise the complexity induced by deviating from the simpler base model and are formulated after the input of a user-defined scaling parameter ...

Studies of spatial population synchrony constitute a central approach for understanding the drivers of ecological dynamics. Recently, identifying the ecological impacts of climate change has emerged as a new important focus in population synchrony studies. However, while it is well known that climatic seasonality and sequential density dependence influences local population dynamics, the role of ...

Reliable quantification of the global mean surface temperature (GMST) response to radiative forcing is essential for assessing the risk of dangerous anthropogenic climate change. We present the statistical foundations for an observation-based approach using a stochastic linear response model that is consistent with the long-range temporal dependence observed in global temperature variability. We ...

Camera traps have become popular labor-efficient and non-invasive tools to study animal populations. The use of camera trap methods has largely focused on large animals and/or animals with identifiable features, with less attention being paid to small mammals, including rodents. Here we investigate the suitability of camera-trap-based abundance indices to monitor population dynamics in two species ...

<i>INTRODUCTION</i>: The point of departure for our paper is that most modern statistical models are built to be flexible enough to model diverse data generating mechanisms. Good statistical practice requires us to limit this flexibility, which is typically controlled by a small number of parameters, to the amount “needed” to model the data at hand. The Bayesian framework provides a natural method ...